《中国康复理论与实践》

《中国康复理论与实践》 ›› 2008, Vol. 14 ›› Issue (02): 141-143.

• 基础研究 • 上一篇    下一篇

基于频带能量和小波包熵的运动意识任务分类研究

任亚莉1; 张爱华2; 郝晓弘2   

  1. 1.陇东学院物理与电子工程学院,甘肃庆阳市 745000;2.兰州理工大学电气工程与信息工程学院,甘肃兰州市 730050
  • 收稿日期:2007-08-29 出版日期:2008-02-01 发布日期:2008-02-01

Study on Classification of Imaginary Hand Movements Based on Band Power and Wavelet Packet Entropy

REN Ya-li, ZHANG Ai-hua, HAO Xiao-hong.   

  1. Physics and Electronic Engineering College, Long Dong University, Qingyang 745000, Gansu, China
  • Received:2007-08-29 Published:2008-02-01 Online:2008-02-01

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被引次数

9

摘要： 目的探讨脑电信号频带能量和小波包熵在识别左右手想象运动中的作用。方法采用脑-计算机接口2003竞赛中Graz科技大学提供的脑电数据,用小波包分解获取8～16Hz脑电信号,计算C3、C4电极脑电信号的频带能量和小波包熵,将其结合作为反应想象左右手运动的特征量,对大脑想象左右手运动任务进行分类。结果对140次实验的测试样本数据分析,最大分类正确率可达87.14%。结论脑电信号频带能量和小波包熵随时间的变化与事件相关去同步和事件相关同步现象相一致,可在线识别左右手想象运动。

关键词: 脑电信号, 频带能量, 小波包熵, 特征提取, 分类

Abstract: Objective To explore the effect of band power and wavelet packet entropy in the recognition of hand imagery.Methods The data gained from brain computer interface competition in 2003 provided by Graz University of Technology.The electroencephalogram(EEG)signals between 8～16 Hz were decomposed by db3 wavelet packet at three levels.The band power(BP)and wavelet packet entropy(WPE)of C3 and C4 were calculated respectively.The BP and WPE were defined as the feature vector.The left and right hand motor imaginary tasks were distinguished.Results The proposed method was applied to the test data set with 140 trails.The satisfactory results were obtained with the highest classification accuracy 87.14%.Conclusion The band power and wavelet packet entropy of EEG changed with time is coincident with event-related desynchronization and event-related synchronization.It can be used to recognize the left and right band motor imaginary tasks.

Key words: electroencephalogram signals, band power, wavelet packet entropy, feature extraction, classification